PURPOSE: Although studies on uveal melanoma (UM) revealed prognostic significance of chromosomal aberrations, they resulted in classification errors in survival prediction. A robust prognostic classifier with strong predictive value and further insight in genes responsible for poor prognosis were obtained by performing a gene-expression profile in tumors of UM patients for which extensive clinical, histopathologic, cytogenetic, and follow-up data were available. Furthermore, the UM microarray expression data were compared with cytogenetic data. METHODS: Gene-expression profiles of 46 UMs were obtained with microchip assays. Data were analyzed with cluster-analysis and predictive analysis of microarrays (PAM) software and validated with real-time PCR. The prognostic significance of UMs with specific molecular signatures was determined. Furthermore, LAP analysis resulted in the identification of differentially expressed chromosomal regions. RESULTS: The primary UMs were classified in two distinct molecular classes with a strong prognostic value (P < 0.001; hazard ratio 7.7). Classifier gene sets for microarray class and disease-free survival were validated with real-time PCR, and the predictive value of the UM class marker set was validated with gene-expression profiles of tumors provided by other institutions, showing a sensitivity of 0.93 and specificity of 1.00 for class II tumors. A locally adaptive statistical procedure identified two regions on the short arm of chromosome 3 with decreased gene-expression in tumors with shorter disease-free survival. CONCLUSIONS: Microarray classification outperforms known prognostic indicators for UM, such as clinical, histopathologic, and cytogenetic parameters. In addition, the identified regions with lower expressed genes on 3p could harbor genes that are responsible for the poor prognosis of patients with UM.
PURPOSE: Although studies on uveal melanoma (UM) revealed prognostic significance of chromosomal aberrations, they resulted in classification errors in survival prediction. A robust prognostic classifier with strong predictive value and further insight in genes responsible for poor prognosis were obtained by performing a gene-expression profile in tumors of UM patients for which extensive clinical, histopathologic, cytogenetic, and follow-up data were available. Furthermore, the UM microarray expression data were compared with cytogenetic data. METHODS: Gene-expression profiles of 46 UMs were obtained with microchip assays. Data were analyzed with cluster-analysis and predictive analysis of microarrays (PAM) software and validated with real-time PCR. The prognostic significance of UMs with specific molecular signatures was determined. Furthermore, LAP analysis resulted in the identification of differentially expressed chromosomal regions. RESULTS: The primary UMs were classified in two distinct molecular classes with a strong prognostic value (P < 0.001; hazard ratio 7.7). Classifier gene sets for microarray class and disease-free survival were validated with real-time PCR, and the predictive value of the UM class marker set was validated with gene-expression profiles of tumors provided by other institutions, showing a sensitivity of 0.93 and specificity of 1.00 for class II tumors. A locally adaptive statistical procedure identified two regions on the short arm of chromosome 3 with decreased gene-expression in tumors with shorter disease-free survival. CONCLUSIONS: Microarray classification outperforms known prognostic indicators for UM, such as clinical, histopathologic, and cytogenetic parameters. In addition, the identified regions with lower expressed genes on 3p could harbor genes that are responsible for the poor prognosis of patients with UM.
Authors: Hima Anbunathan; Ruth Verstraten; Arun D Singh; J William Harbour; Anne M Bowcock Journal: Clin Cancer Res Date: 2019-06-21 Impact factor: 12.531
Authors: Brandon T Nguyen; Ryan S Kim; Maria E Bretana; Eric Kegley; Amy C Schefler Journal: Graefes Arch Clin Exp Ophthalmol Date: 2017-11-28 Impact factor: 3.117
Authors: J William Harbour; Manuel Paez-Escamilla; Louis Cai; Scott D Walter; James J Augsburger; Zelia M Correa Journal: Am J Ophthalmol Date: 2018-09-07 Impact factor: 5.258
Authors: T Huibertus van Essen; Sake I van Pelt; Mieke Versluis; Inge H G Bronkhorst; Sjoerd G van Duinen; Marina Marinkovic; Wilma G M Kroes; Claudia A L Ruivenkamp; Shruti Shukla; Annelies de Klein; Emine Kiliç; J William Harbour; Gregorius P M Luyten; Pieter A van der Velden; Rob M Verdijk; Martine J Jager Journal: Br J Ophthalmol Date: 2014-08-21 Impact factor: 4.638
Authors: Matthew G Field; Jeffim N Kuznetsov; Parker L Bussies; Louie Z Cai; Karam A Alawa; Christina L Decatur; Stefan Kurtenbach; J William Harbour Journal: Clin Cancer Res Date: 2019-07-08 Impact factor: 12.531
Authors: Scott D Walter; Daniel L Chao; William Feuer; Joyce Schiffman; Devron H Char; J William Harbour Journal: JAMA Ophthalmol Date: 2016-07-01 Impact factor: 7.389
Authors: Chandrani Chattopadhyay; Dae Won Kim; Dan S Gombos; Junna Oba; Yong Qin; Michelle D Williams; Bita Esmaeli; Elizabeth A Grimm; Jennifer A Wargo; Scott E Woodman; Sapna P Patel Journal: Cancer Date: 2016-03-15 Impact factor: 6.860